Alignment Unlocks Complementarity: A Framework for Multiview Circuit Representation Learning

• URL: http://arxiv.org/abs/2509.20968v1
• Date: Thu, 25 Sep 2025 10:12:04 GMT
• Title: Alignment Unlocks Complementarity: A Framework for Multiview Circuit Representation Learning
• Abstract summary: Multiview learning on Boolean circuits holds immense promise, as different graph-based representations offer complementary structural and semantic information.<n>We introduce MixGate, a framework built on a principled training curriculum that teaches the model a shared, function-aware representation space.<n>We show that our alignment-first strategy transforms masked modeling from an ineffective technique into a powerful performance driver.
• Score: 12.528410977116438
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multiview learning on Boolean circuits holds immense promise, as different graph-based representations offer complementary structural and semantic information. However, the vast structural heterogeneity between views, such as an And-Inverter Graph (AIG) versus an XOR-Majority Graph (XMG), poses a critical barrier to effective fusion, especially for self-supervised techniques like masked modeling. Naively applying such methods fails, as the cross-view context is perceived as noise. Our key insight is that functional alignment is a necessary precondition to unlock the power of multiview self-supervision. We introduce MixGate, a framework built on a principled training curriculum that first teaches the model a shared, function-aware representation space via an Equivalence Alignment Loss. Only then do we introduce a multiview masked modeling objective, which can now leverage the aligned views as a rich, complementary signal. Extensive experiments, including a crucial ablation study, demonstrate that our alignment-first strategy transforms masked modeling from an ineffective technique into a powerful performance driver.

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